Effective Techniques for Code Plagiarism Detection Using Python

Plagiarism in coding is a serious issue, especially in education and software development. Detecting copied code can be challenging because even small changes in variable names or formatting can make similar code look different. Modern plagiarism checkers, like Codequiry, employ a combination of advanced techniques to compare code, identify similarities, and ensure originality across multiple programming languages. As a Python Code Plagiarism Checker, Codequiry effectively analyzes Python submissions while maintaining accuracy across various other languages.

Common Techniques for Code Plagiarism Detection

• String Matching: Compares code line by line to find exact matches.

• AST (Abstract Syntax Tree): Analyzes the structure of code to detect logical similarities, regardless of formatting.

• Tokenization: Break down the code into smaller parts to identify recurring patterns.

• Machine Learning: Uses AI to identify subtle forms of plagiarism that traditional methods may miss.

• Levenshtein Distance: Measures the similarity between two code samples by counting the minimum changes required.

Why Codequiry is the Ideal Solution

Ensuring original code is essential for fairness and learning. Codequiry’s plagiarism engine integrates these techniques and goes beyond by incorporating AI-based semantic analysis to detect complex similarities. As an AI Code Plagiarism Checker, it checks both internal and external sources accurately, ensuring a thorough assessment of originality. With multi-language support, Codequiry identifies unoriginal content effectively and helps maintain coding integrity.